Money laundering and other illegal/anti-competitive financial activities, often performed by previously unknown money service businesses (MSBs), are a tremendous threat to financial institutions. The failure to detect and identify previously unknown MSBs can be highly detrimental to the healthy function of these financial institutions, and is often linked to terrorism and/or other criminal activities. This is one of the most challenging problems that financial institutions face in the modern world. In order to address this issue, various methodologies have been proposed that combine computer algorithms with subsequent human expert analysis to detect, identify, investigate, and report suspicious MSB-like activities. Despite the advances in such semi-automated, semi-manual methodologies, detecting and identifying previously unknown MSBs is still a very difficult and time consuming process, requiring various human experts to carefully investigate many thousands of cases involving potentially suspicious customers and transactions that have been selected from millions of customers and transactions. These methodologies are simply not suitable in light of the volume of database information that must be processed every day. The transaction characteristics that typically define a MSB include: the use of money orders, the use of traveler's checks, the use of electronic money transmissions, unusual check cashing activities, unusual currency exchanges, currency dealing transactions, and stored value scenarios.
Thus, what is still needed in the art are automated methods and systems for the detection and identification of previously unknown MSB transactions, such that human experts can be more efficiently utilized in investigation and reporting roles, thereby streamlining the law enforcement process.